{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:00  :  1808.85  :  1766.004\n",
      "00:05  :  1795.36  :  1741.8380000000002\n",
      "00:10  :  1766.32  :  1714.406\n",
      "00:15  :  1748.26  :  1697.832\n",
      "00:20  :  1749.86  :  1680.766\n",
      "00:25  :  1717.41  :  1667.8200000000002\n",
      "00:30  :  1705.32  :  1650.04\n",
      "00:35  :  1704.37  :  1631.8139999999999\n",
      "00:40  :  1678.86  :  1620.328\n",
      "00:45  :  1668.22  :  1616.918\n",
      "00:50  :  1656.33  :  1602.856\n",
      "00:55  :  1651.54  :  1592.8980000000001\n",
      "01:00  :  1630.3  :  1585.996\n",
      "01:05  :  1610.78  :  1573.24\n",
      "01:10  :  1600.4  :  1555.2759999999998\n",
      "01:15  :  1588.47  :  1549.5880000000002\n",
      "01:20  :  1581.51  :  1534.3219999999997\n",
      "01:25  :  1575.98  :  1527.9840000000002\n",
      "01:30  :  1575.93  :  1538.614\n",
      "01:35  :  1559.56  :  1503.8220000000001\n",
      "01:40  :  1552.19  :  1501.152\n",
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      "02:45  :  1500.21  :  1449.4679999999998\n",
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      "04:05  :  1496.89  :  1452.3380000000002\n",
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      "05:20  :  1823.43  :  1756.0459999999998\n",
      "05:25  :  1867.54  :  1788.0500000000002\n",
      "05:30  :  1914.2  :  1823.894\n",
      "05:35  :  1974.57  :  1881.04\n",
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      "15:25  :  2855.94  :  2688.5\n",
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      "17:25  :  3014.41  :  2851.978\n",
      "17:30  :  3026.19  :  2884.4179999999997\n",
      "17:35  :  3114.37  :  2940.144\n",
      "17:40  :  3145.86  :  2987.1720000000005\n",
      "17:45  :  3158.57  :  3024.0640000000003\n",
      "17:50  :  3160.41  :  3032.006\n",
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      "18:10  :  3213.91  :  3082.7079999999996\n",
      "18:15  :  3214.25  :  3105.6499999999996\n",
      "18:20  :  3214.56  :  3110.4800000000005\n",
      "18:25  :  3230.06  :  3110.63\n",
      "18:30  :  3213.27  :  3105.05\n",
      "18:35  :  3198.3  :  3084.666\n",
      "18:40  :  3165.26  :  3067.684\n",
      "18:45  :  3185.36  :  3059.512\n",
      "18:50  :  3182.07  :  3085.7879999999996\n",
      "18:55  :  3125.41  :  3072.2799999999997\n",
      "19:00  :  3123.9  :  3070.482\n",
      "19:05  :  3066.72  :  3039.574\n",
      "19:10  :  3079.7  :  3036.6139999999996\n",
      "19:15  :  3077.81  :  3012.38\n",
      "19:20  :  3065.05  :  3018.78\n",
      "19:25  :  3043.23  :  2994.816\n",
      "19:30  :  3038.18  :  2984.768\n",
      "19:35  :  3025.41  :  2960.858\n",
      "19:40  :  3010.04  :  2969.2219999999998\n",
      "19:45  :  2971.6  :  2933.004\n",
      "19:50  :  2960.5  :  2914.3859999999995\n",
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      "20:00  :  2907.44  :  2869.9580000000005\n",
      "20:05  :  2871.93  :  2833.498\n",
      "20:10  :  2853.1  :  2822.172\n",
      "20:15  :  2817.12  :  2790.1000000000004\n",
      "20:20  :  2783.68  :  2775.786\n",
      "20:25  :  2781.56  :  2756.9860000000003\n",
      "20:30  :  2787.61  :  2697.1980000000003\n",
      "20:35  :  2719.76  :  2691.214\n",
      "20:40  :  2698.2  :  2682.504\n",
      "20:45  :  2644.67  :  2647.334\n",
      "20:50  :  2619.65  :  2614.7540000000004\n",
      "20:55  :  2614.94  :  2596.4080000000004\n",
      "21:00  :  2581.03  :  2562.0699999999997\n",
      "21:05  :  2542.24  :  2513.9139999999998\n",
      "21:10  :  2486.97  :  2493.142\n",
      "21:15  :  2475.59  :  2479.11\n",
      "21:20  :  2472.49  :  2474.864\n",
      "21:25  :  2462.56  :  2446.546\n",
      "21:30  :  2439.05  :  2442.826\n",
      "21:35  :  2380.38  :  2427.902\n",
      "21:40  :  2385.53  :  2413.764\n",
      "21:45  :  2347.06  :  2389.046\n",
      "21:50  :  2371.3  :  2376.8419999999996\n",
      "21:55  :  2335.07  :  2366.494\n",
      "22:00  :  2334.67  :  2339.9139999999998\n",
      "22:05  :  2278.79  :  2319.462\n",
      "22:10  :  2267.62  :  2286.3160000000003\n",
      "22:15  :  2227.62  :  2260.67\n",
      "22:20  :  2211.03  :  2241.446\n",
      "22:25  :  2190.52  :  2223.752\n",
      "22:30  :  2161.86  :  2196.106\n",
      "22:35  :  2149.82  :  2175.614\n",
      "22:40  :  2148.27  :  2148.674\n",
      "22:45  :  2098.53  :  2121.98\n",
      "22:50  :  2081.83  :  2108.0260000000003\n",
      "22:55  :  2038.77  :  2082.174\n",
      "23:00  :  2039.82  :  2068.138\n",
      "23:05  :  2012.06  :  2024.132\n",
      "23:10  :  1994.64  :  1992.922\n",
      "23:15  :  1946.31  :  1981.036\n",
      "23:20  :  1943.95  :  1954.3919999999998\n",
      "23:25  :  1968.3  :  1938.052\n",
      "23:30  :  1952.46  :  1906.3519999999999\n",
      "23:35  :  1918.29  :  1882.592\n",
      "23:40  :  1885.93  :  1875.324\n",
      "23:45  :  1893.45  :  1856.542\n",
      "23:50  :  1858.06  :  1832.7800000000002\n",
      "23:55  :  1829.17  :  1773.196\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "p = 5\n",
    "dict={}\n",
    "dates = ['25-11-2017.csv','26-11-2017.csv','27-11-2017.csv','28-11-2017.csv','29-11-2017.csv','30-11-2017.csv']\n",
    "for i in range(len(dates)):\n",
    "    dict[dates[i]] = []\n",
    "    time = []\n",
    "    for d in csv.DictReader(open(dates[i]), delimiter=','):\n",
    "        time.append(d['time'])\n",
    "        dict[dates[i]].append(float(d['value']))\n",
    "n = len(dict[dates[len(dates)-1]])\n",
    "#actualload = [load[i] for i in range(len(load))]\n",
    "forecast =[0]*n\n",
    "for i in range(n):\n",
    "    for j in range(p):\n",
    "        forecast[i] += dict[dates[j]][i]/p \n",
    "for i in range(n):\n",
    "    print(time[i],\" : \",dict[dates[len(dates)-1]][i],\" : \",forecast[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S2apPQdGVlJJ31sURm17E+qOZVFRbGBrizo/zR/PssoPsSMjlrgndOZRSyAc3RWgJASDm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\nxkRYUTDGGBPxP+Cv6V9KnmBNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea06f77898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dict[dates[len(dates)-1]])\n",
    "plt.plot(forecast)\n",
    "plt.legend(['load', 'forecast'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
